Have you wondered how information spreads on Twitter/X, how Instagram influencers are identified, and how different actors in an online community collaborate or confront one and another? There are the sorts of questions that can be best answered using social network analysis (SNA) or broadly network analysis. In network analysis of internet communities, we visualize and quantify the structure of social relationships and information flows.
In this tutorial, we will work with a pre-generated network from OSOME Network Tool[note: the tool has been depreciated as of spring 2025].
Here, you can see a retweet/repost network based on #Ukraine from July 9, 2022 to August 8, 2022. In this network, a pair of users represents a repost relationship. That is, two users are connected to one and another if one reposts or is reposted by the other. For simplicity, the graph below only shows users who at least twice reposted or were reposted by others.
Guess how the size and color of a node is determined.
Where do we begin to visualize a network? It all starts with an edgelist. The table below shows a portion of the edgelist.
An edgelist shows all edges in a network along with attributes of the edges. An edge is a pair of relationship between two nodes (in this case, users). An edge can be directed: for example, A reposts B will be expressed as User A → User B, whereas B reposts A is expressed as User B → User A. But, in some cases, an edge is undirected. Think about your Facebook relationships. If user A is a friend of user B. By default, user B is also connected to user A.
In the edgelist below. The column from_label lists the Twitter/X users who reposted. The column to_label shows those users who were on the receiving end of the reposts (i.e., users who were reposted by others). The weight column is edge weight, referring to the number of reposting that occurred between the same pair of users.
In our example, a node is a Twitter/X user. Below is a list of nodes, with their id, labels, and attributes (e.g., size, color).